MADMX

MADMX: a strategy for maximal dense motif extraction. We develop, analyze and experiment with a new tool, called madmx, which extracts frequent motifs, possibly including don’t care characters, from biological sequences. We introduce density, a simple and flexible measure for bounding the number of don’t cares in a motif, defined as the ratio of solid (i.e., different from don’t care) characters to the total length of the motif. By extracting only maximal dense motifs, madmx reduces the output size and improves performance, while enhancing the quality of the discoveries. The efficiency of our approach relies on a newly defined combining operation, dubbed fusion, which allows for the construction of maximal dense motifs in a bottom-up fashion, while avoiding the generation of nonmaximal ones. We provide experimental evidence of the efficiency and the quality of the motifs returned by madmx.


References in zbMATH (referenced in 6 articles )

Showing results 1 to 6 of 6.
Sorted by year (citations)

  1. Grossi, Roberto; Menconi, Giulia; Pisanti, Nadia; Trani, Roberto; Vind, Søren: Motif trie: an efficient text index for pattern discovery with don’t cares (2018)
  2. Federico, Maria; Peterlongo, Pierre; Pisanti, Nadia; Sagot, Marie-France: \textscRime: repeat identification (2014)
  3. Parida, Laxmi; Pizzi, Cinzia; Rombo, Simona E.: Irredundant tandem motifs (2014)
  4. Rombo, Simona E.: Extracting string motif bases for quorum higher than two (2012)
  5. Grossi, Roberto; Pietracaprina, Andrea; Pisanti, Nadia; Pucci, Geppino; Upfal, Eli; Vandin, Fabio: MADMX: A novel strategy for maximal dense motif extraction (2010) ioport
  6. Grossi, Roberto; Pietracaprina, Andrea; Pisanti, Nadia; Pucci, Geppino; Upfal, Eli; Vandin, Fabio: MADMX: A novel strategy for maximal dense motif extraction (2009) ioport